Reducing Error Rate for Eye-Tracking System by Applying SVM

Ahmed, Nafiz Ishtiaque; Nasrin, Fatema

doi:10.1007/978-981-19-2347-0_4

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…SVM is frequently employed in ML modeling of regression or categorization issues [59]. SVR employs similar categorization techniques as the SVM, along with some minor modifications [59][60][61][62][63]. Throughout the regression instance, a range of tolerance (ε) is given to the SVM as a prediction of what the issue might have previously required.…”

Section: Support Vector Machinementioning

confidence: 99%

The Impact of ECAP Parameters on the Structural and Mechanical Behavior of Pure Mg: A Combination of Experimental and Machine Learning Approaches

et al. 2023

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Commercial pure Mg specimens were processed through equal channel angular pressing (ECAP) using two dies with die angles of 90° and 120°. Mg billets were processed up to four passes via different route types. Machine learning (ML) techniques were adopted to estimate the ECAP parameters and verify the experimental findings. Several ML techniques were employed to estimate the effect ECAP parameters of pure Mg on microstructural evolution, Vicker’s microhardness (HV), and tensile properties for ECAP billets and their as-annealed (AA) counterparts. Electron back-scatter diffraction (EBSD) was applied to determine the structural evolution and crystallographic texture both prior to and following the ECAP process for the Mg billets. EBSD analysis showed that route Bc is the most effective route in grain refinement, and four passes of route Bc experienced a significant refinement of 86% compared to the AA condition. Furthermore, the crystallographic texture showed that four passes of route Bc produced the most robust texture that was greater than 26.21 times random. ML findings revealed that the grain size demonstrated a strong correlation of −0.67 with rising number of passes, while ϕ affected the grain size strongly with 0.83. When adopting a 90°-die to accumulate the plastic strain up to 4Bc, the subsequent HV was indeed 111% higher than that of the AA equivalent. From ML findings it was clear that the number of passes was the most significant parameter on the Mg HV values, while ECAP channel angle (ϕ) revealed high correlation factor with HV values as well. Furthermore, four passes of route Bc with ϕ = 90° and 120° led to a significant increase of the tensile strength by 44.7%% and 35.7%, respectively, compared to the AA counterpart. ML findings revealed that the tensile strength was affected by the increasing number of passes with a strong correlation of 0.81, while affecting ductility moderately with 0.47.

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Section: Support Vector Machinementioning

confidence: 99%

The Impact of ECAP Parameters on the Structural and Mechanical Behavior of Pure Mg: A Combination of Experimental and Machine Learning Approaches

et al. 2023

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“…The SVM approach [22] is widely used for ML modeling of classification or regression problems. With a few minor exceptions, the support vector machine regression (SVR) utilizes the same concepts as the SVM for classification [23][24][25][26]. In the case of regression, a margin of tolerance (ε) is specified as a rough approximation to the SVM that the issue would have already requested.…”

Section: Soft Claymentioning

confidence: 99%

Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach

2022

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Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world. These regions are vital and attractive for population and governments development. Soft soil is classified as problematic soil owing to sustaining low shear strength and high settlement under structures. Constructing structures and/or infrastructures on soft soil is a considerable risk that needs great attention from structural engineers. The bearing capacity of structure foundations on soft soil depends mainly on their undrained shear strength. This soil feature strongly influences the selection of appropriate soil improvement methods. However, determining undrained shear strength is very difficult, costly, and time-consuming, especially for sensitive clay. Consequently, extracting undisturbed samples of sensitive clay faces several difficulties on construction sites. In this research, accurate field-tested data were fed to advanced machine learning models to predict the undrained shear strength of the sensitive clay to save hard effort, time, repeated laboratory testing, and costs. In this context, a dataset of 111 geotechnical testing points were collected based on laboratory and field examinations of the soil’s key features. These features included the water content, liquid limit, dry unit weight, plasticity index, consistency index, void ratio, specific gravity, and pocket penetration shear. Several machine learning algorithms were adopted to provide the soft clay modeling, including the linear, Gaussian process regression, ensemble and regression trees, and the support vector regression. The coefficient of determination was mainly used to assess the performance of each predictive model. The achieved results revealed that the support vector regression model attained the most accurate prediction for soil undrained shear strength. These outcomes lay the groundwork for evaluating soil shear strength characteristics in a practical, fast, and low-cost way.

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“…The advancements in modern technology have enabled the field of neuroscience to make significant progress in understanding the workings of this complex organ, especially in the domain of bio-signals [1]. Technologies like EOG and EEG have allowed researchers to gain unprecedented insights into the workings of the brain, and the application of machine learning and deep learning techniques has helped to analyze these signals and extract meaningful information from them [2]. This has enormous implications for healthcare, with neuroscience playing a pivotal role in developing new diagnoses and treatments for neurological disorders.…”

Section: Introductionmentioning

confidence: 99%

Approach to Understanding Brain Disorders and Improving Healthcare Outcomes

Hossain¹

2023

Preprint

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The convergence of bio-signals in neuroscience has been a topic of interest for researchers for many years. Neuroscience has made significant strides in understanding the human brain and its functions. The use of bio-signals, such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET), has been instrumental in advancing the field of neuroscience. Integrating these bio-signals allows for a more comprehensive understanding of brain function. Integrating bio-signals has been particularly useful in studying brain disorders such as epilepsy, Alzheimer's, and schizophrenia. For example, EEG has been used to diagnose epilepsy and monitor patients' responses to treatment. MEG has been used to study the neural mechanisms underlying Alzheimer's disease. fMRI has been used to study the effects of antipsychotic medication on brain function in patients with schizophrenia. Integrating bio-signals has also helped study cognitive processes, such as attention, perception, and memory. For example, EEG has been used to study attentional processes, while fMRI has been used to study memory retrieval. Integrating these bio-signals has allowed researchers to gain a more complete understanding of these cognitive processes. The convergence of bio-signals has also been helpful in the development of brain-computer interfaces (BCIs) and virtual reality (VR) systems. BCIs allow individuals to control devices using their brain activity. The integration of multiple bio-signals can improve the accuracy and reliability of BCIs. VR systems allow individuals to interact with virtual environments. The integration of bio-signals can provide a more immersive VR experience. Despite the advantages of integrating bio-signals, there are also challenges. One challenge is the need for accurate and precise synchronization of the different bio-signals. This requires specialized equipment and software. Another challenge is the need for data analysis techniques to handle the integrated bio-signals complexity and variability.

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Reducing Error Rate for Eye-Tracking System by Applying SVM

Cited by 6 publications

References 23 publications

The Impact of ECAP Parameters on the Structural and Mechanical Behavior of Pure Mg: A Combination of Experimental and Machine Learning Approaches

The Impact of ECAP Parameters on the Structural and Mechanical Behavior of Pure Mg: A Combination of Experimental and Machine Learning Approaches

Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach

Approach to Understanding Brain Disorders and Improving Healthcare Outcomes

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